Product Health Metrics AI Prompts

Analyze the DAU/MAU ratio (stickiness) for this product and identify improvement opportunities. DAU and MAU data: {{engagement_data}} Product type: {{product_type}} Time period: {{period}} 1. Stickiness calculation: - DAU/MAU ratio: daily_active_users / monthly_active_users x 100% - Industry benchmarks by product type: - Social/messaging: 40-70% (high daily habit) - Productivity/SaaS: 20-40% - E-commerce: 5-15% (purchase frequency dependent) - Gaming: 20-40% - How does this product compare to benchmark? 2. Trend analysis: - Plot DAU/MAU over the last 12 months - Is stickiness improving, declining, or stable? - Is DAU growing faster or slower than MAU? (DAU growing faster = improving stickiness) - Identify any inflection points and what caused them 3. Stickiness by segment: - DAU/MAU for: new users (< 30 days), established users (30-90 days), power users (> 90 days) - DAU/MAU by acquisition channel, plan type, company size - Which segment has the highest stickiness? What drives it? 4. Usage pattern analysis: - What is the distribution of active days per user per month? - Are users clustered into daily users, weekly users, and monthly users? - What does the 'weekly user' segment use the product for? (May reveal a different use case) 5. Stickiness drivers: - Which features correlate most strongly with daily return visits? - Do users who complete {{onboarding_action}} have higher stickiness? - Is there a usage threshold that separates sticky from non-sticky users? Return: stickiness metrics, benchmark comparison, trend analysis, segment breakdown, and stickiness driver analysis.

Step 1: North Star definition - define or validate the North Star Metric for this product. Decompose it into Level 1 and Level 2 input metrics. Assign owners to each leaf metric. Step 2: Growth accounting - apply the growth accounting framework to the last 12 months. Compute the quick ratio trend. Diagnose whether this is a new user, retention, or resurrection problem. Step 3: Funnel audit - map the full acquisition-to-activation funnel. Identify the top 2 drop-off points. Segment the funnel by device, channel, and cohort. Step 4: Retention analysis - build the cohort retention matrix. Compute Day 1, Day 7, and Day 30 retention by cohort. Identify whether newer cohorts are improving or declining. Step 5: Feature adoption - for the top 3 features, compute adoption rates and time-to-first-use. Identify which feature has the strongest correlation with 30-day retention. Step 6: User segmentation - segment users into at least 4 behavioral groups (Champions, At-risk, Dormant, New). Size each segment and compute its contribution to revenue or activity. Step 7: Recommendations and roadmap - synthesize findings into a prioritized list of 5 product and analytics recommendations. For each: the problem it addresses, the expected impact, and the measurement plan.

Design a product health monitoring framework for {{product_name}}. Product type: {{product_type}} (SaaS, mobile app, marketplace, etc.) Business model: {{business_model}} Current data available: {{data_sources}} 1. AARRR metrics framework: Define the key metric for each stage: - Acquisition: how are users finding and signing up for the product? (CAC, sign-up rate, channel mix) - Activation: are new users experiencing the core value? (activation rate, time-to-value, onboarding completion) - Retention: are users coming back? (Day 1/7/30 retention, MAU/DAU ratio, churn rate) - Revenue: are users paying? (ARPU, MRR, conversion to paid, expansion revenue) - Referral: are users sharing? (NPS, referral rate, viral coefficient) 2. Leading vs lagging indicators: For each AARRR stage: identify one leading indicator (predicts future performance) and one lagging indicator (confirms past performance) 3. North Star Metric: - Define the single metric that best captures value delivered to users - It should be: measurable, predictive of revenue, influenceable by the team - Decompose it: what inputs drive the North Star? (Weekly Active Users x actions per user, for example) 4. Alert thresholds: - For each health metric: define the threshold that triggers an alert (e.g. Day 7 retention drops > 5% WoW) - Define monitoring frequency: real-time, daily, or weekly per metric 5. Dashboard layout: - Top section: North Star Metric + 4 AARRR headline numbers with WoW change - Middle section: retention cohort heatmap, funnel conversion rates - Bottom section: acquisition channel mix, revenue breakdown Return: AARRR metric definitions, North Star decomposition, alert thresholds, and dashboard spec.